def __init__(self,
m_mins,
m_maxs,
s_mins,
s_maxs,
length,
type,
handle_tol,
handle_noise,
rest_state,
perturbation=None):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.length = length
self.type = type
self.handle_tol = handle_tol
self.handle_tol_sq = handle_tol * handle_tol
self.handle_noise = handle_noise
self.rest_state = rest_state
self.perturbation = perturbation
if self.perturbation == "BrokenTool1":
self.length_breakpoint = 0.5
self.angle_breakpoint = np.pi * 0.5
self.reset()
def __init__(self,
m_mins,
m_maxs,
s_mins,
s_maxs,
lengths,
angle_shift,
rest_state,
n_dmps=3,
n_bfs=6,
timesteps=50,
gui=False):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.lengths = lengths
self.angle_shift = angle_shift
self.rest_state = rest_state
self.reset()
self.gui = gui
# DMP PARAMETERS
self.n_dmps = n_dmps
self.n_bfs = n_bfs
self.timesteps = timesteps
self.max_params = np.array([300.] * self.n_bfs * self.n_dmps +
[1.] * self.n_dmps)
self.motor_dmp = MyDMP(n_dmps=self.n_dmps,
n_bfs=self.n_bfs,
timesteps=self.timesteps,
max_params=self.max_params)
def __init__(self,
synth,
m_mins,
m_maxs,
s_mins,
s_maxs,
m_used,
s_used,
n_dmps,
n_bfs,
dmp_move_steps,
dmp_max_param,
sensory_traj_samples,
audio,
diva_path=None):
self.m_mins = m_mins
self.m_maxs = m_maxs
self.s_mins = s_mins
self.s_maxs = s_maxs
self.m_used = m_used
self.s_used = s_used
self.n_dmps = n_dmps
self.n_bfs = n_bfs
self.dmp_move_steps = dmp_move_steps
self.dmp_max_param = dmp_max_param
self.samples = array(linspace(-1, self.dmp_move_steps - 1,
sensory_traj_samples + 1),
dtype=int)[1:]
self.audio = audio
self.f0 = 1.
self.pressure = 1.
self.voicing = 1.
if self.audio:
import pyaudio
self.pa = pyaudio.PyAudio()
self.stream = self.pa.open(format=pyaudio.paFloat32,
channels=1,
rate=11025,
output=True)
if synth == "octave":
self.synth = DivaOctaveSynth(diva_path)
elif synth == "matlab":
self.synth = DivaMatlabSynth()
else:
raise NotImplementedError
self.art = array(
[0.] * 10 + [self.f0, self.pressure, self.voicing]
) # 13 articulators is a constant from diva_synth.m in the diva source code
self.max_params = array([1.] * self.n_dmps + [self.dmp_max_param] *
self.n_bfs * self.n_dmps + [1.] * self.n_dmps)
self.dmp = DmpPrimitive(dmps=self.n_dmps,
bfs=self.n_bfs,
timesteps=self.dmp_move_steps)
Environment.__init__(self, self.m_mins, self.m_maxs, self.s_mins,
self.s_maxs)
def __init__(self, robot, motors, move_duration,
m_mins, m_maxs, s_mins, s_maxs):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.robot = robot
self.motors = [m.name for m in getattr(self.robot, motors)]
self.move_duration = move_duration
def __init__(self, m_mins, m_maxs, s_mins, s_maxs):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
# DMP PARAMETERS
self.n_dmps = 4
self.n_bfs = 7
self.timesteps = 25
self.max_params = np.array([300.] * self.n_bfs * self.n_dmps +
[1.] * self.n_dmps)
self.motor_dmp = MyDMP(n_dmps=self.n_dmps,
n_bfs=self.n_bfs,
timesteps=self.timesteps,
max_params=self.max_params)
# SPACES
self.hand = 30 * [0.]
self.joystick1 = 20 * [0.]
self.joystick2 = 20 * [0.]
self.ergo = 20 * [0.]
self.ball = 20 * [0.]
self.light = 10 * [0.]
self.sound = 10 * [0.]
# CONTEXT
self.ergo_theta = 0.
self.ball_theta = 0.
self.current_context = [self.ergo_theta, self.ball_theta]
def __init__(self, env, context_mode):
self.context_mode = context_mode
self.env = env
if self.context_mode["mode"] == 'mdmsds':
Environment.__init__(
self,
np.hstack(
(self.env.conf.m_mins, self.context_mode['dm_bounds'][0])),
np.hstack(
(self.env.conf.m_maxs, self.context_mode['dm_bounds'][1])),
np.hstack(
(self.env.conf.s_mins, self.context_mode['ds_bounds'][0])),
np.hstack(
(self.env.conf.s_maxs, self.context_mode['ds_bounds'][1])))
elif self.context_mode["mode"] == 'mcs':
Environment.__init__(
self, self.env.conf.m_mins, self.env.conf.m_maxs,
np.hstack((self.context_mode['context_sensory_bounds'][0],
self.env.conf.s_mins)),
np.hstack((self.context_mode['context_sensory_bounds'][1],
self.env.conf.s_maxs)))
else:
raise NotImplementedError
self.current_motor_position = None
self.current_sensori_position = None
self.reset()
def __init__(self, m_mins, m_maxs, s_mins, s_maxs,
object_shape, rest_state):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.object_shape = object_shape
self.rest_state = rest_state
self.reset()
def __init__(self, m_mins, m_maxs, s_mins, s_maxs, box_m_mins, box_m_maxs):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.box_m_mins = box_m_mins
self.box_m_maxs = box_m_maxs
self.center = (np.array(self.box_m_mins) + np.array(self.box_m_maxs)) / 2.
self.reset()
def __init__(self, m_mins, m_maxs, s_mins, s_maxs, object_tol2,
rest_state):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.object_tol2_sq = object_tol2 * object_tol2
self.rest_state = rest_state
self.reset()
def __init__(self, m_mins, m_maxs, s_mins, s_maxs, object_shape,
rest_state):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.object_shape = object_shape
self.rest_state = rest_state
self.reset()
def __init__(self, m_mins, m_maxs, s_mins, s_maxs,
object_tol2, rest_state):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.object_tol2_sq = object_tol2 * object_tol2
self.rest_state = rest_state
self.reset()
def __init__(self, m_mins, m_maxs, s_mins, s_maxs, lengths, angle_shift, rest_state):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.lengths = lengths
self.angle_shift = angle_shift
self.rest_state = rest_state
self.reset()
def __init__(self,
env_cls,
env_cfg,
m_mins,
m_maxs,
s_mins,
s_maxs,
n_bfs,
move_steps,
n_dynamic_motor_dims,
n_dynamic_sensori_dims,
max_params,
n_sensori_traj_points=None,
motor_traj_type="DMP",
sensori_traj_type="samples",
optim_initial_position=False,
optim_end_position=False,
default_motor_initial_position=None,
default_motor_end_position=None,
default_sensori_initial_position=None,
default_sensori_end_position=None):
self.env = env_cls(**env_cfg)
self.n_bfs = n_bfs
self.n_motor_traj_points = self.n_bfs + optim_initial_position + optim_end_position
self.n_sensori_traj_points = n_sensori_traj_points or self.n_bfs
self.move_steps = move_steps
self.n_dynamic_motor_dims = n_dynamic_motor_dims
self.n_dynamic_sensori_dims = n_dynamic_sensori_dims
self.max_params = max_params
self.motor_traj_type = motor_traj_type
self.sensori_traj_type = sensori_traj_type
self.optim_initial_position = optim_initial_position
self.optim_end_position = optim_end_position
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.s_traj = None
if self.motor_traj_type == "DMP":
self.init_motor_DMP(optim_initial_position, optim_end_position,
default_motor_initial_position,
default_motor_end_position)
else:
raise NotImplementedError
if self.sensori_traj_type == "DMP":
self.init_sensori_DMP(optim_initial_position, optim_end_position,
default_sensori_initial_position,
default_sensori_end_position)
elif self.sensori_traj_type == "samples":
self.samples = np.array(np.linspace(
-1, self.move_steps - 1, self.n_sensori_traj_points + 1),
dtype=int)[1:]
elif self.sensori_traj_type == "end_point":
self.end_point = self.move_steps - 1
else:
raise NotImplementedError
def __init__(self, m_mins, m_maxs, s_mins, s_maxs, species, noise,
rest_state):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.species = species
self.noise = noise
self.rest_state = rest_state
self.reset()
def __init__(self, m_mins, m_maxs, s_mins, s_maxs,
object_tol_hand, object_tol_tool, bounds):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.object_tol_hand_sq = object_tol_hand * object_tol_hand
self.object_tol_tool_sq = object_tol_tool * object_tol_tool
self.bounds = bounds
self.reset()
def __init__(self, m_mins, m_maxs, s_mins, s_maxs, box_m_mins, box_m_maxs):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.box_m_mins = box_m_mins
self.box_m_maxs = box_m_maxs
self.center = (np.array(self.box_m_mins) +
np.array(self.box_m_maxs)) / 2.
self.reset()
def __init__(self, m_mins, m_maxs, s_mins, s_maxs, lengths, angle_shift,
rest_state):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.lengths = lengths
self.angle_shift = angle_shift
self.rest_state = rest_state
self.reset()
def __init__(self, m_mins, m_maxs, s_mins, s_maxs,
species, noise, rest_state):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.species = species
self.noise = noise
self.rest_state = rest_state
self.reset()
def __init__(self, m_mins, m_maxs, s_mins, s_maxs, length, handle_tol,
rest_state):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.length = length
self.handle_tol = handle_tol
self.handle_tol_sq = handle_tol * handle_tol
self.rest_state = rest_state
self.reset()
def __init__(self, m_mins, m_maxs, s_mins, s_maxs,
length, handle_tol, rest_state):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.length = length
self.handle_tol = handle_tol
self.handle_tol_sq = handle_tol * handle_tol
self.rest_state = rest_state
self.reset()
def __init__(self, env_cls, env_conf, context_mode):
self.rest_position = context_mode['rest_position']
self.context_mode = context_mode
self.env = env_cls(**env_conf)
Environment.__init__(self,
np.hstack((self.env.conf.m_mins, self.context_mode['dm_bounds'][0])),
np.hstack((self.env.conf.m_maxs, self.context_mode['dm_bounds'][1])),
np.hstack((self.env.conf.s_mins, self.context_mode['ds_bounds'][0])),
np.hstack((self.env.conf.s_maxs, self.context_mode['ds_bounds'][1])))
self.reset()
def __init__(self, m_mins, m_maxs, s_mins, s_maxs,
size, initial_position, ball_type="magnetic", color='y', random_ball_noise=0.2):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.ball_type = ball_type
self.size = size
self.size_sq = size * size
self.color = color
self.initial_position = initial_position
self.random_ball_noise = random_ball_noise
self.reset()
def __init__(self, s_mins, s_maxs, envs_cls, envs_cfg, combined_s):
self.envs = [cls(**cfg) for cls,cfg in zip(envs_cls, envs_cfg)]
self.n_envs = len(self.envs)
self.n_params_envs = [len(env.conf.m_dims) for env in self.envs]
self.n_params_envs_cumsum = np.cumsum([0] + self.n_params_envs)
self.combined_s = combined_s
config = dict(m_mins=[dim for env in self.envs for dim in env.conf.m_mins],
m_maxs=[dim for env in self.envs for dim in env.conf.m_maxs],
s_mins=s_mins,
s_maxs=s_maxs)
Environment.__init__(self, **config)
def __init__(self, m_mins, m_maxs, s_mins, s_maxs, arm_lengths,
arm_angle_shift, arm_rest_state, ball_size,
ball_initial_position):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.arm_lengths = arm_lengths
self.arm_angle_shift = arm_angle_shift
self.arm_rest_state = arm_rest_state
self.ball_size = ball_size
self.size_sq = ball_size * ball_size
self.ball_initial_position = ball_initial_position
self.trajectory = list()
self.reset()
def __init__(self,
synth,
m_mins, m_maxs, s_mins, s_maxs,
m_used,
s_used,
n_dmps,
n_bfs,
dmp_move_steps,
dmp_max_param,
sensory_traj_samples,
audio,
diva_path=None):
self.m_mins = m_mins
self.m_maxs = m_maxs
self.s_mins = s_mins
self.s_maxs = s_maxs
self.m_used = m_used
self.s_used = s_used
self.n_dmps = n_dmps
self.n_bfs = n_bfs
self.dmp_move_steps = dmp_move_steps
self.dmp_max_param = dmp_max_param
self.samples = array(linspace(-1, self.dmp_move_steps-1, sensory_traj_samples + 1), dtype=int)[1:]
self.audio = audio
self.f0 = 1.
self.pressure = 1.
self.voicing = 1.
if self.audio:
import pyaudio
self.pa = pyaudio.PyAudio()
self.stream = self.pa.open(format=pyaudio.paFloat32,
channels=1,
rate=11025,
output=True)
if synth == "octave":
self.synth = DivaOctaveSynth(diva_path)
elif synth == "matlab":
self.synth = DivaMatlabSynth()
else:
raise NotImplementedError
self.art = array([0.]*10 + [self.f0, self.pressure, self.voicing]) # 13 articulators is a constant from diva_synth.m in the diva source code
self.max_params = array([1.] * self.n_dmps + [self.dmp_max_param] * self.n_bfs * self.n_dmps + [1.] * self.n_dmps)
self.dmp = DmpPrimitive(dmps=self.n_dmps, bfs=self.n_bfs, timesteps=self.dmp_move_steps)
Environment.__init__(self, self.m_mins, self.m_maxs, self.s_mins, self.s_maxs)
def __init__(self, m_mins, m_maxs, s_mins, s_maxs, arm_lengths,
arm_angle_shift, arm_rest_state, arrow_size,
arrow_initial_pose):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.arm_lengths = arm_lengths
self.arm_angle_shift = arm_angle_shift
self.arm_rest_state = arm_rest_state
self.arrow_size = arrow_size
self.size_sq = arrow_size * arrow_size
self.arrow_initial_pose = arrow_initial_pose
self.initial_pose = arrow_initial_pose
self.trajectory = list()
self.reset()
def __init__(self, m_mins, m_maxs, s_mins, s_maxs, length, handle_tol, handle_noise, rest_state, perturbation=None):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.length = length
self.handle_tol = handle_tol
self.handle_tol_sq = handle_tol * handle_tol
self.handle_noise = handle_noise
self.rest_state = rest_state
self.perturbation = perturbation
if self.perturbation == "BrokenTool1":
self.length_breakpoint = 0.5
self.angle_breakpoint = np.pi * 0.5
self.reset()
def __init__(self, env_cls, env_cfg,
m_mins, m_maxs, s_mins, s_maxs,
n_bfs, n_motor_traj_points, n_sensori_traj_points, move_steps,
n_dynamic_motor_dims, n_dynamic_sensori_dims, max_params,
motor_traj_type="DMP", sensori_traj_type="DMP",
optim_initial_position=True, optim_end_position=True, default_motor_initial_position=None, default_motor_end_position=None,
default_sensori_initial_position=None, default_sensori_end_position=None,
gui=False):
self.env = env_cls(**env_cfg)
self.n_bfs = n_bfs
self.n_motor_traj_points = n_motor_traj_points
self.n_sensori_traj_points = n_sensori_traj_points
self.move_steps = move_steps
self.n_dynamic_motor_dims = n_dynamic_motor_dims
self.n_dynamic_sensori_dims = n_dynamic_sensori_dims
self.max_params = max_params
self.motor_traj_type = motor_traj_type
self.sensori_traj_type = sensori_traj_type
self.optim_initial_position = optim_initial_position
self.optim_end_position = optim_end_position
self.gui = gui
self.n_mvt = 1
if self.gui:
plt.ion()
self.ax = plt.subplot()
plt.gcf().set_size_inches(12., 12., forward=True)
plt.gca().set_aspect('equal')
#plt.show(block=False)
plt.draw()
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.s_traj = None
if self.motor_traj_type == "DMP":
self.init_motor_DMP(optim_initial_position, optim_end_position, default_motor_initial_position, default_motor_end_position)
else:
raise NotImplementedError
if self.sensori_traj_type == "DMP":
self.init_sensori_DMP(optim_initial_position, optim_end_position, default_sensori_initial_position, default_sensori_end_position)
elif self.sensori_traj_type == "samples":
self.samples = np.array(np.linspace(-1, self.move_steps-1, self.n_sensori_traj_points + 1), dtype=int)[1:]
else:
raise NotImplementedError
def update(self, mov, audio=True):
s = Environment.update(self, mov)
if self.audio and audio:
sound = self.sound_wave(self.art_traj)
self.stream.write(sound.astype(float32).tostring())
#time.sleep(1)
#print "Sound sent", sound, len(sound)
return s
def __init__(self, m_mins, m_maxs, s_mins, s_maxs, top_env_cls, lower_env_cls, top_env_cfg, lower_env_cfg, fun_m_lower, fun_s_lower, fun_s_top):
self.top_env = top_env_cls(**top_env_cfg)
self.lower_env = lower_env_cls(**lower_env_cfg)
self.n_params_lower_env = len(self.lower_env.conf.m_dims)
self.fun_m_lower = fun_m_lower
self.fun_s_lower = fun_s_lower
self.fun_s_top = fun_s_top
config = dict(m_mins=m_mins,
m_maxs=m_maxs,
s_mins=s_mins,
s_maxs=s_maxs)
Environment.__init__(self, **config)
def __init__(self, m_mins, m_maxs, s_mins, s_maxs, arm_lengths,
arm_angle_shift, arm_rest_state, ball_size,
ball_initial_position, tool_length, tool_initial_pose):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.arm_lengths = arm_lengths
self.arm_angle_shift = arm_angle_shift
self.arm_rest_state = arm_rest_state
self.ball_size = ball_size
self.size_sq = ball_size * ball_size
self.ball_initial_position = ball_initial_position
self.tool_length = tool_length
self.tool_initial_pose = tool_initial_pose
self.initial_pose = np.concatenate(
[ball_initial_position, tool_initial_pose]).tolist()
self.trajectory = list()
self.reset()
def __init__(self,
synth,
m_mins,
m_maxs,
s_mins,
s_maxs,
m_used,
s_used,
audio,
diva_path=None):
self.m_mins = m_mins
self.m_maxs = m_maxs
self.s_mins = s_mins
self.s_maxs = s_maxs
self.m_used = m_used
self.s_used = s_used
self.audio = audio
self.f0 = 1.
self.pressure = 1.
self.voicing = 1.
if self.audio:
import pyaudio
self.pa = pyaudio.PyAudio()
self.stream = self.pa.open(format=pyaudio.paFloat32,
channels=1,
rate=11025,
output=True)
if synth == "octave":
self.synth = DivaOctaveSynth(diva_path)
elif synth == "matlab":
self.synth = DivaMatlabSynth()
else:
raise NotImplementedError
self.art = array(
[0.] * 10 + [self.f0, self.pressure, self.voicing]
) # 13 articulators is a constant from diva_synth.m in the diva source code
Environment.__init__(self, self.m_mins, self.m_maxs, self.s_mins,
self.s_maxs)
def __init__(self,
m_mins,
m_maxs,
s_mins,
s_maxs,
size,
initial_position,
ball_type="magnetic",
color='y',
random_ball_noise=0.2):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.ball_type = ball_type
self.size = size
self.size_sq = size * size
self.color = color
self.initial_position = initial_position
self.random_ball_noise = random_ball_noise
self.reset()
def __init__(self, robot, motors, move_duration, t_reset,
m_mins, m_maxs, s_mins, s_maxs):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.robot = robot
self.motors = []
for i_mot, mot in enumerate(getattr(self.robot, motors)):
self.motors.append(mot.name)
if mot.name in constraints.keys():
m_mins[i_mot] = constraints[mot.name][0]
m_maxs[i_mot] = constraints[mot.name][1]
time.sleep(4)
rest_position = []
# angle_limits = []
for m in self.robot.motors:
rest_position.append(m.present_position)
self.rest_position = array(rest_position)
self.move_duration = move_duration
self.t_reset = t_reset
def __init__(self, robot, motors, move_duration, t_reset, m_mins, m_maxs,
s_mins, s_maxs):
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.robot = robot
self.motors = []
for i_mot, mot in enumerate(getattr(self.robot, motors)):
self.motors.append(mot.name)
if mot.name in constraints.keys():
m_mins[i_mot] = constraints[mot.name][0]
m_maxs[i_mot] = constraints[mot.name][1]
time.sleep(4)
rest_position = []
# angle_limits = []
for m in self.robot.motors:
rest_position.append(m.present_position)
self.rest_position = array(rest_position)
self.move_duration = move_duration
self.t_reset = t_reset
def __init__(self,
synth,
m_mins, m_maxs, s_mins, s_maxs,
m_used,
s_used,
audio,
diva_path=None):
self.m_mins = m_mins
self.m_maxs = m_maxs
self.s_mins = s_mins
self.s_maxs = s_maxs
self.m_used = m_used
self.s_used = s_used
self.audio = audio
self.f0 = 1.
self.pressure = 1.
self.voicing = 1.
if self.audio:
import pyaudio
self.pa = pyaudio.PyAudio()
self.stream = self.pa.open(format=pyaudio.paFloat32,
channels=1,
rate=11025,
output=True)
if synth == "octave":
self.synth = DivaOctaveSynth(diva_path)
elif synth == "matlab":
self.synth = DivaMatlabSynth()
else:
raise NotImplementedError
self.art = array([0.]*10 + [self.f0, self.pressure, self.voicing]) # 13 articulators is a constant from diva_synth.m in the diva source code
Environment.__init__(self, self.m_mins, self.m_maxs, self.s_mins, self.s_maxs)
def __init__(self, env_cls, env_cfg,
m_mins, m_maxs, s_mins, s_maxs,
n_bfs, move_steps,
n_dynamic_motor_dims, n_dynamic_sensori_dims, max_params,
motor_traj_type="DMP", sensori_traj_type="samples",
optim_initial_position=False, optim_end_position=False, default_motor_initial_position=None, default_motor_end_position=None,
default_sensori_initial_position=None, default_sensori_end_position=None):
self.env = env_cls(**env_cfg)
self.n_bfs = n_bfs
self.n_motor_traj_points = self.n_bfs
self.n_sensori_traj_points = self.n_bfs
self.move_steps = move_steps
self.n_dynamic_motor_dims = n_dynamic_motor_dims
self.n_dynamic_sensori_dims = n_dynamic_sensori_dims
self.max_params = max_params
self.motor_traj_type = motor_traj_type
self.sensori_traj_type = sensori_traj_type
self.optim_initial_position = optim_initial_position
self.optim_end_position = optim_end_position
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
self.s_traj = None
if self.motor_traj_type == "DMP":
self.init_motor_DMP(optim_initial_position, optim_end_position, default_motor_initial_position, default_motor_end_position)
else:
raise NotImplementedError
if self.sensori_traj_type == "DMP":
self.init_sensori_DMP(optim_initial_position, optim_end_position, default_sensori_initial_position, default_sensori_end_position)
elif self.sensori_traj_type == "samples":
self.samples = np.array(np.linspace(-1, self.move_steps-1, self.n_sensori_traj_points + 1), dtype=int)[1:]
elif self.sensori_traj_type == "end_point":
self.end_point = self.move_steps-1
else:
raise NotImplementedError
def __init__(self, env_cls, env_conf, context_mode):
self.context_mode = context_mode
self.env = env_cls(**env_conf)
if self.context_mode["mode"] == 'mdmsds':
Environment.__init__(self,
np.hstack((self.env.conf.m_mins, self.context_mode['dm_bounds'][0])),
np.hstack((self.env.conf.m_maxs, self.context_mode['dm_bounds'][1])),
np.hstack((self.env.conf.s_mins, self.context_mode['ds_bounds'][0])),
np.hstack((self.env.conf.s_maxs, self.context_mode['ds_bounds'][1])))
elif self.context_mode["mode"] == 'mcs':
Environment.__init__(self,
self.env.conf.m_mins,
self.env.conf.m_maxs,
np.hstack((self.context_mode['context_sensory_bounds'][0], self.env.conf.s_mins)),
np.hstack((self.context_mode['context_sensory_bounds'][1], self.env.conf.s_maxs)))
else:
raise NotImplementedError
self.current_motor_position = None
self.current_sensori_position = None
self.reset()
def __init__(self, gui=False, audio=False):
self.t = 0
# ARM CONFIG
arm_cfg = dict(m_mins=[-1.] * 3 * 7,
m_maxs=[1.] * 3 * 7,
s_mins=[-1.] * 3 * 50,
s_maxs=[1.] * 3 * 50,
lengths=[0.5, 0.3, 0.2],
angle_shift=0.5,
rest_state=[0., 0., 0.],
n_dmps=3,
n_bfs=6,
timesteps=50,
gui=gui)
# SOUND CONFIG
self.v_o = list(np.log2([500, 900]))
self.v_y = list(np.log2([300, 1700]))
self.v_u = list(np.log2([300, 800]))
self.v_e = list(np.log2([400, 2200]))
self.v_i = list(np.log2([300, 2300]))
self.vowels = dict(o=self.v_o,
y=self.v_y,
u=self.v_u,
e=self.v_e,
i=self.v_i)
# Retrieve caregiver sounds and trajectories from json
self.rospack = RosPack()
with open(
join(self.rospack.get_path('pobax_playground'), 'config',
'human_sounds.pickle')) as f:
self.full_human_motor_traj, self.full_human_sounds_traj = pickle.load(
f)
self.human_sounds = self.full_human_sounds_traj.keys()
rospy.loginfo('Voice node using the word %s for culbuto name' %
self.human_sounds[0])
def compress_sound_traj(sound):
assert (len(sound) == 100)
f1s = sound[:50]
f3s = sound[50:]
return np.append(f1s[np.array([0, 12, 24, 37, 49])],
f3s[np.array([0, 12, 24, 37, 49])])
#reduce number of sounds
self.human_sounds = ['eyu', 'oey', 'eou', 'oyi']
#random.shuffle(self.human_sounds)
print self.human_sounds
self.human_sounds_traj = dict()
self.human_sounds_traj_std = dict()
self.best_vocal_errors = {}
self.best_vocal_errors_evolution = []
for hs in self.human_sounds:
self.best_vocal_errors[hs] = 10.
self.human_sounds_traj[hs] = compress_sound_traj(
self.full_human_sounds_traj[hs])
self.human_sounds_traj_std[hs] = [
d - 8.5 for d in self.human_sounds_traj[hs][:5]
] + [d - 10.25 for d in self.human_sounds_traj[hs][5:]]
self.sound_tol = 0.4
# DIVA CONFIG
diva_cfg = dict(
m_mins=np.array([-1, -1, -1, -1, -1, -1, -1]),
m_maxs=np.array([1, 1, 1, 1, 1, 1, 1]),
s_mins=np.array([7.5, 9.25]),
s_maxs=np.array([9.5, 11.25]),
m_used=range(7),
s_used=range(1, 3),
rest_position_diva=list([0] * 7),
audio=audio,
diva_use_initial=True,
diva_use_goal=True,
used_diva=list([True] * 7),
n_dmps_diva=7,
n_bfs_diva=2,
move_steps=50,
)
self.arm = ArmEnvironment(**arm_cfg)
self.diva = DivaEnvironment(**diva_cfg)
self.timesteps = 50
# OBJECTS CONFIG
self.caregiver_gives_obj_factor = 0.01
self.tool_length = 0.5
self.handle_tol = 0.2
self.handle_tol_sq = self.handle_tol * self.handle_tol
self.handle_noise = 0.
self.object_tol_hand = 0.2
self.object_tol_hand_sq = self.object_tol_hand * self.object_tol_hand
self.object_tol_tool = 0.2
self.object_tol_tool_sq = self.object_tol_tool * self.object_tol_tool
self.diva_traj = None
self.produced_sound = None
Environment.__init__(self,
m_mins=[-1.] * (21 + 28),
m_maxs=[1.] * (21 + 28),
s_mins=[-1.] * 56,
s_maxs=[1.] * 56)
self.current_tool = [-0.5, 0., 0.5, 0.]
self.current_toy1 = [0.5, 0.5, 0.]
#self.current_toy2 = [0.7, 0.7, 0.]
#self.current_toy3 = [0.9, 0.9, 0.]
self.current_caregiver = [0., 1.7]
self.reset()
self.compute_tool()
self.purge_logs()
if self.arm.gui:
self.init_plot()
self.count_diva = 0
self.count_arm = 0
self.count_tool = 0
self.count_toy1_by_tool = 0
#self.count_toy2_by_tool = 0
#self.count_toy3_by_tool = 0
self.count_toy1_by_hand = 0
#self.count_toy2_by_hand = 0
#self.count_toy3_by_hand = 0
self.count_parent_give_label = 0
self.count_parent_give_object = 0
self.count_produced_sounds = {}
for hs in self.human_sounds:
self.count_produced_sounds[hs] = 0
self.time_arm = 0.
self.time_diva = 0.
self.time_arm_per_it = 0.
self.time_diva_per_it = 0.
def __init__(self, gui=False, audio=False):
self.t = 0
# ARM CONFIG
arm_cfg = dict(
m_mins=[-1.] * 3 * 7,
m_maxs=[1.] * 3 * 7,
s_mins=[-1.] * 3 * 50,
s_maxs=[1.] * 3 * 50,
lengths=[0.5, 0.3, 0.2],
angle_shift=0.5,
rest_state=[0., 0., 0.],
n_dmps=3,
n_bfs=6,
timesteps=50,
gui=gui)
# SOUND CONFIG
self.v_o = list(np.log2([500, 900]))
self.v_y = list(np.log2([300, 1700]))
self.v_u = list(np.log2([300, 800]))
self.v_e = list(np.log2([400, 2200]))
self.v_i = list(np.log2([300, 2300]))
self.vowels = dict(o=self.v_o, y=self.v_y, u=self.v_u, e=self.v_e, i=self.v_i)
self.human_sounds = ['oey', 'uye', 'iuo', 'eyu', 'eou', 'yeo']
random.shuffle(self.human_sounds)
print "human sounds", self.human_sounds
def compute_s_sound(sound):
s1 = self.vowels[sound[0]]
s2 = [(self.vowels[sound[0]][0] + self.vowels[sound[1]][0]) / 2., (self.vowels[sound[0]][1] + self.vowels[sound[1]][1]) / 2.]
s3 = self.vowels[sound[1]]
s4 = [(self.vowels[sound[1]][0] + self.vowels[sound[2]][0]) / 2., (self.vowels[sound[1]][1] + self.vowels[sound[2]][1]) / 2.]
s5 = self.vowels[sound[2]]
rdm = 0.0 * (2.*np.random.random((1,10))[0] - 1.)
return list(rdm + np.array([f[0] for f in [s1, s2, s3, s4, s5]] + [f[1] for f in [s1, s2, s3, s4, s5]]))
self.human_sounds_traj = dict()
self.human_sounds_traj_std = dict()
self.best_vocal_errors = {}
self.best_vocal_errors_evolution = []
for hs in self.human_sounds:
self.best_vocal_errors[hs] = 10.
self.human_sounds_traj[hs] = compute_s_sound(hs)
self.human_sounds_traj_std[hs] = [d - 8.5 for d in self.human_sounds_traj[hs][:5]] + [d - 10.25 for d in self.human_sounds_traj[hs][5:]]
self.sound_tol = 0.4
# DIVA CONFIG
diva_cfg = dict(
m_mins = np.array([-1, -1, -1, -1, -1, -1, -1]),
m_maxs = np.array([1, 1, 1, 1, 1, 1, 1]),
s_mins = np.array([ 7.5, 9.25]),
s_maxs = np.array([ 9.5 , 11.25]),
m_used = range(7),
s_used = range(1, 3),
rest_position_diva = list([0]*7),
audio = audio,
diva_use_initial = True,
diva_use_goal = True,
used_diva = list([True]*7),
n_dmps_diva = 7,
n_bfs_diva = 2,
move_steps = 50,
)
self.arm = ArmEnvironment(**arm_cfg)
self.diva = DivaEnvironment(**diva_cfg)
self.timesteps = 50
# OBJECTS CONFIG
self.caregiver_gives_obj_factor = 0.01
self.tool_length = 0.5
self.handle_tol = 0.2
self.handle_tol_sq = self.handle_tol * self.handle_tol
self.handle_noise = 0.
self.object_tol_hand = 0.2
self.object_tol_hand_sq = self.object_tol_hand * self.object_tol_hand
self.object_tol_tool = 0.2
self.object_tol_tool_sq = self.object_tol_tool * self.object_tol_tool
self.diva_traj = None
self.produced_sound = None
Environment.__init__(self,
m_mins= [-1.] * (21+28),
m_maxs= [1.] * (21+28),
s_mins= [-1.] * 80,
s_maxs= [1.] * 80)
self.current_tool = [-0.5, 0., 0.5, 0.]
self.current_toy1 = [0.5, 0.5, 0.]
self.current_toy2 = [0.7, 0.7, 0.]
self.current_toy3 = [0.9, 0.9, 0.]
self.current_caregiver = [0., 1.7]
self.reset()
self.compute_tool()
self.purge_logs()
if self.arm.gui:
self.init_plot()
self.count_diva = 0
self.count_arm = 0
self.count_tool = 0
self.count_toy1_by_tool = 0
self.count_toy2_by_tool = 0
self.count_toy3_by_tool = 0
self.count_toy1_by_hand = 0
self.count_toy2_by_hand = 0
self.count_toy3_by_hand = 0
self.count_parent_give_label = 0
self.count_parent_give_object = 0
self.count_produced_sounds = {}
for hs in self.human_sounds:
self.count_produced_sounds[hs] = 0
self.time_arm = 0.
self.time_diva = 0.
self.time_arm_per_it = 0.
self.time_diva_per_it = 0.
def update(self, mov, audio=True):
s = Environment.update(self, mov)
if self.audio and audio:
self.play_sound(self.art_traj)
return s
def update(self, mov, audio=True):
s = Environment.update(self, mov)
if self.audio and audio:
self.play_sound(self.art_traj)
return s
def update(self, m_ag, reset=False, log=False):
return Environment.update(self, m_ag, reset=True, log=log)
def __init__(self,
env_cls,
env_cfg,
m_mins,
m_maxs,
s_mins,
s_maxs,
n_bfs,
n_motor_traj_points,
n_sensori_traj_points,
move_steps,
n_dynamic_motor_dims,
n_dynamic_sensori_dims,
max_params,
motor_traj_type="DMP",
sensori_traj_type="DMP",
optim_initial_position=True,
optim_end_position=True,
default_motor_initial_position=None,
default_motor_end_position=None,
default_sensori_initial_position=None,
default_sensori_end_position=None,
gui=False):
self.env = env_cls(**env_cfg)
self.n_bfs = n_bfs
self.n_motor_traj_points = n_motor_traj_points
self.n_sensori_traj_points = n_sensori_traj_points
self.move_steps = move_steps
self.n_dynamic_motor_dims = n_dynamic_motor_dims
self.n_dynamic_sensori_dims = n_dynamic_sensori_dims
self.max_params = max_params
self.motor_traj_type = motor_traj_type
self.sensori_traj_type = sensori_traj_type
self.optim_initial_position = optim_initial_position
self.optim_end_position = optim_end_position
self.gui = gui
self.n_mvt = 0
if self.gui:
plt.ion()
self.ax = plt.subplot()
plt.gcf().set_size_inches(12., 12., forward=True)
plt.gca().set_aspect('equal')
#plt.show(block=False)
plt.draw()
Environment.__init__(self, m_mins, m_maxs, s_mins, s_maxs)
if self.motor_traj_type == "DMP":
self.init_motor_DMP(optim_initial_position, optim_end_position,
default_motor_initial_position,
default_motor_end_position)
else:
raise NotImplementedError
if self.sensori_traj_type == "DMP":
self.init_sensori_DMP(optim_initial_position, optim_end_position,
default_sensori_initial_position,
default_sensori_end_position)
elif self.sensori_traj_type == "samples":
self.samples = np.array(np.linspace(
-1, self.move_steps - 1, self.n_sensori_traj_points + 1),
dtype=int)[1:]
else:
raise NotImplementedError
def update(self, m_ag, reset=False, log=False):
return Environment.update(self, m_ag, reset=True, log=log)
def __init__(self, m_mins, m_maxs, s_mins, s_maxs, m_used, s_used,
rest_position_diva, audio, diva_use_initial, diva_use_goal,
used_diva, n_dmps_diva, n_bfs_diva, move_steps):
self.m_mins = m_mins
self.m_maxs = m_maxs
self.s_mins = s_mins
self.s_maxs = s_maxs
self.m_used = m_used
self.s_used = s_used
self.rest_position_diva = rest_position_diva
self.audio = audio
self.diva_use_initial = diva_use_initial
self.diva_use_goal = diva_use_goal
self.used_diva = used_diva
self.n_dmps_diva = n_dmps_diva
self.n_bfs_diva = n_bfs_diva
self.move_steps = move_steps
self.f0 = 1.
self.pressure = 1.
self.voicing = 1.
if (os.environ.has_key('AVAKAS') and os.environ['AVAKAS']):
self.audio = False
if self.audio:
self.pa = pyaudio.PyAudio()
self.stream = self.pa.open(format=pyaudio.paFloat32,
channels=1,
rate=11025,
output=True)
self.synth = DivaSynth()
self.art = array(
[0.] * 10 + [self.f0, self.pressure, self.voicing]
) # 13 articulators is a constant from diva_synth.m in the diva source code
self.max_params = []
if self.diva_use_initial:
self.max_params = self.max_params + [1.] * self.n_dmps_diva
self.max_params = self.max_params + [
300.
] * self.n_bfs_diva * self.n_dmps_diva
if self.diva_use_goal:
self.max_params = self.max_params + [1.] * self.n_dmps_diva
self.max_params = np.array(self.max_params)
self.dmp = MyDMP(n_dmps=self.n_dmps_diva,
n_bfs=self.n_bfs_diva,
timesteps=self.move_steps,
use_init=self.diva_use_initial,
max_params=self.max_params)
self.default_m = zeros(self.n_dmps_diva * self.n_bfs_diva +
self.n_dmps_diva * self.diva_use_initial +
self.n_dmps_diva * self.diva_use_goal)
self.default_m_traj = self.compute_motor_command(self.default_m)
self.default_sound = self.synth.execute(self.art.reshape(-1, 1))[0]
self.default_formants = None
self.default_formants = self.compute_sensori_effect(
self.default_m_traj)
Environment.__init__(self, self.m_mins, self.m_maxs, self.s_mins,
self.s_maxs)
